title: A stochastic catastrophe model using two-fluid model parameters to investigate traffic safety on urban arterials.
abstract: During the last few decades, the two-fluid model and its two parameters have been widely used in transportation engineering to represent the quality of operational traffic service on urban arterials. Catastrophe models have also often been used to describe traffic flow on freeway sections. This paper demonstrates the possibility of developing a pro-active network screening tool that estimates the crash rate using a stochastic cusp catastrophe model with the two-fluid model's parameters as inputs. The paper investigates the analogy in logic behind the two-fluid model and the catastrophe model using straightforward graphical illustrations. The paper then demonstrates the application of two-fluid model parameters to a stochastic catastrophe model designed to estimate the level of safety on urban arterials. Current road safety management, including network safety screening, is post-active rather than pro-active in the sense that an existing hotspot must be identified before a safety improvement program can be implemented. This paper suggests that a stochastic catastrophe model can help us to become more pro-active by helping us to identify urban arterials that currently show an acceptable level of safety, but which are vulnerable to turning into crash hotspots. We would then be able to implement remedial actions before hotspots develop.
authors.affiliation: Department of Civil and Geological Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. peter.park@usask.ca
authors.forename: Peter Y
authors.initials: PY
authors.lastname: Park
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abdel-Aty
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D003196:Computer Graphics; D004779:Environment Design; D006801:Humans; D015233:Models, Statistical; D011156:Population Density; D012449:Safety / Q000706:statistics & numerical data*; D013269:Stochastic Processes; D014505:Urban Population / Q000706:statistics & numerical data*